Embodiments of the present invention relate to a Hough processor, further embodiments relate to an image analyzing system for tracking a pupil with a Hough processor, a method for a Hough processing and a computer program for executing the method.
Various image recognition systems or image evaluation systems, as e.g. 2D image analyzing tools or 3D image analyzers may be based on the Hough transformation as explained in the following.
Hough processors serve for the execution of a Hough transformation by means of which geometric patterns like straight lines or circles or also only segments of such geometric patterns can be recognized. During recognition, it is typically emanated from gradient images or monochrome images or binary edge images. By means of the Hough transformation, a transfer of a two-dimensional initial image into a multi-dimensional accumulator room occurs, which is also referred to as a Hough room. In this room, the searched structure is phrased in a parameter image or the Hough room is stretched over the parameters. According to the complexity of the structure to be detected, the Hough room has a plurality of dimensions. Thus, a Hough room typically comprises two dimensions for the recognition (angle between x-axis and normal on the straight line and distance plumb foot point from the origin, cf. Hessian normal form); regarding a Hough room for the recognition of circles, typically three dimensions (two times position coordinates of the circle midpoint, once circle radius) are available, while a Hough room for the recognition of ellipses typically comprises five dimensions (two times position coordinates ellipsis midpoint, two times ellipsis diameter, once inclination angle). Insofar, the Hough transformation is characterized in that an image to be processed is transferred to an n-dimensional Hough room. The searched geometric features could also be referred to as Hough features. These are recognizable according to their frequency distribution in the Hough room (can also be referred to as accumulator room).
The Hough transformation constitutes the basis in order to efficiently and reliably recognize geometric structures by means of a Hough transformation algorithm. In practice, for example the detection of an ellipsis or ellipsis form, as e.g. regarding a pupil or an iris, or also other distinctive structures in the eye (e.g. eye lids) is an important application, whereby, however, it should be noted that the execution of Hough transformation algorithms is very complex in calculating. This results in the fact that the real-time capability of Hough transformation algorithms is limited. A further disadvantage resulting therefrom is that an embodiment of a Hough transformation algorithm typically presupposes specific Hough processors or generally very efficient processors so that the implementation of a Hough recognition algorithm by means of simple and/or cost efficient processors, but also FPGAs (Field Programmable Gate Arrays, integrated switch with programmable logic switch elements) is difficult or even impossible.
Improvements regarding the performance have been achieved by a so-called parallel Hough transformation, as it is e.g. described in the patent specification of DE 10 2005 047 160 B4. Regarding this parallel Hough transformation, however, only a binary result relating to an image coordinate (position of the structure), but not the measure for the accordance of the searched structure or further structure features, can be detected. Furthermore, a flexible adjustment of the transformation core during the ongoing operation is not possible, that limits the suitability regarding dynamic image contents (e.g. small and big pupils). Thus, the transformation core is not reconfigurable so that other structures cannot be recognized during the ongoing operation.
Therefore, there is the need for an improved concept.